Semantic Building Blocks in Genetic Programming

In this paper we present a new mechanism for studying the impact of subtree crossover in terms of semantic building blocks. This approach allows us to completely and compactly describe the semantic action of crossover, and provide insight into what does (or doesn’t) make crossover effective. Our results make it clear that a very high proportion of crossover events (typically over 75% in our experiments) are guaranteed to perform no immediately useful search in the semantic space. Our findings also indicate a strong correlation between lack of progress and high proportions of fixed contexts. These results then suggest several new, theoretically grounded, research areas

Enumerating Building Block Semantics in Genetic Programming

This report provides a collection of definitions for the semantics of sub-trees and contexts as manipulated by standard sub-tree crossover in genetic programming (GP). These definitions allow us to completely and compactly describe the exact semantics of the components manipulated by sub-tree crossover, and the semantic results of those interactions. Sub- sequent work shows how these definitions can be used to collect valuable data about the available diversity in a GP population and the opportunities available to sub-tree crossover

Exploring the Effects of CAM Therapy Compared to Opioid Administration in a Hospital Setting

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1229/thumbnail.jp

RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning

An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator protein for example give rise to cystic fibrosis. Unfortunately performing InDel mutations on physical proteins and studying their effects is a time prohibitive process. Consequently, modeling InDels computationally can supplement and inform wet lab experiments. In this work, we make use of our data sets of exhaustive double InDel mutations for three proteins which we computationally generated using a robotics inspired inverse kinematics approach available in Rosetta. We develop and train a neural network, RoseNet, on several structural and energetic metrics output by Rosetta during the mutant generation process. We explore and present how RoseNet is able to emulate the exhaustive data set using deep learning methods, and show to what extent it can predict Rosetta metrics for unseen mutant sequences with two InDels. RoseNet achieves a Pearson correlation coefficient median accuracy of 0.775 over all Rosetta scores for the largest protein. Furthermore, a sensitivity analysis is performed to determine the necessary quantity of data required to accurately emulate the structural scores for computationally generated mutants. We show that the model can be trained on minimal data (<50%) and still retain a high level of accuracy.Comment: Presented at Computational Structural Bioinformatics Workshop 202